Dataset Information

Identification of ferroptosis-associated biomarkers in Stanford type A aortic dissection based on machine learning.

ABSTRACT:

Background

Stanford type A aortic dissection (STAAD) is a serious cardiovascular disease with a high mortality rate. Ferroptosis is closely associated with various diseases, including cardiovascular disease. However, the role of ferroptosis in the progression of STAAD remains unclear.

Methods

Gene expression profiles of GSE52093, GSE98770, and GSE153434 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE) were performed to determine the ferroptosis-associated characteristic genes in STAAD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy. Furthermore, immune cell infiltrations were analyzed using the CIBERSORT algorithm. Drug sensitivity analysis was conducted based on the CellMiner database.

Results

A total of 65 differentially expressed ferroptosis-associated genes were screened. DAZAP1 and GABARAPL2 were identified as valuable diagnostic biomarkers for STAAD. A nomogram with high accuracy and reliability was constructed as a diagnostic tool for STAAD. Furthermore, immune infiltration analysis suggested that monocytes were higher in the STAAD group compared with the control group. DAZAP1 was positively correlated with monocytes, whereas GABARAPL2 was negatively correlated with monocytes. Pan-cancer analysis showed that DAZAP1 and GABARAPL2 were closely associated with the prognosis of various cancers. In addition, some antitumor drugs might be useful for the treatment of STAAD.

Conclusion

DAZAP1 and GABARAPL2 might serve as potential diagnostic biomarkers for STAAD. Meanwhile, DAZAP1 and GABARAPL2 might be related to cancer and STAAD in terms of ferroptosis, which provides insights into developing new therapeutic approaches for STAAD.

SUBMITTER: Pan H

PROVIDER: S-EPMC10251017 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Identification of ferroptosis-associated biomarkers in Stanford type A aortic dissection based on machine learning.

Pan Hao H Lu Wei W Liu Zhifei Z Wang Yu Y

American journal of translational research 20230515 5

<h4>Background</h4>Stanford type A aortic dissection (STAAD) is a serious cardiovascular disease with a high mortality rate. Ferroptosis is closely associated with various diseases, including cardiovascular disease. However, the role of ferroptosis in the progression of STAAD remains unclear.<h4>Methods</h4>Gene expression profiles of GSE52093, GSE98770, and GSE153434 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA), l ...[more]

PMID: 37303674

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Project description:Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear. Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein-protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm. Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell-cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group. Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.

Project description:IntroductionStanford type A aortic dissection (TAAD) is one of the lethal macrovascular diseases caused by the invasion of blood into the media layer of ascending aortic wall. Inflammation, smooth muscle dysfunction, and extracellular matrix (ECM) degradation were regarded as the major pathology in affected tissue. However, the expression pattern and its regulation especially through circular RNAs (circRNAs) as an overall characteristic of TAAD molecular pathology remain unclear.MethodsWe employed CIRCexplorer2 to identify circRNAs based on the RNA sequencing (RNA-seq) data of human ascending aortic tissues to systematically assess the role of circRNA in the massive alterations of gene expression in TAAD aortas. The key circRNAs were determined by LASSO model and functionally annotated by competing endogenous RNAs (ceRNA) network and co-analysis with mRNA profile. The expression level and diagnostic capability of the 4 key circRNAs in peripheral serum were confirmed by real-time polymerase chain reaction (RT-PCR).ResultsThe 4 key circRNAs, namely circPTGR1 (chr9:114341075-114348445[-]), circNOX4 (chr11:89069012-89106660[-]), circAMN1 (chr12:31854796-31862359[-]) and circUSP3 (chr15:63845913-63855207[+]), demonstrated a high power to discriminate between TAAD and control tissues, suggesting that these molecules stand for a major difference between the tissues at gene regulation level. Functionally, the ceRNA network of circRNA-miRNA-mRNA predicted by the online databases, combining gene set enrichment analysis (GSEA) and cell component prediction, revealed that the identified circRNAs covered all the aspects of primary TAAD pathology, centralized with increasing inflammatory factors and cells, and ECM destruction and loss of vascular inherent cells along with the circRNAs. Importantly, we validated the high concentration and diagnostic capability of the 4 key circRNAs in the peripheral serum in TAAD patients.DiscussionThis study reinforces the vital status of circRNAs in TAAD and the possibility of serving as promising diagnostic biomarkers.

Project description:BackgroundThe correlation between rhabdomyolysis and postoperative acute kidney injury has been reported in several surgical procedures. As a good predictor of rhabdomyolysis-related acute kidney injury, an elevated serum myoglobin level was often observed after total aortic arch replacement combined with frozen elephant trunk implantation. However, the correlation between serum myoglobin and acute kidney injury in such patients had not been established.MethodsTotally 398 stanford type A aortic dissection patients who underwent total aortic arch replacement combined with frozen elephant trunk implantation were enrolled in this retrospective study. The correlations between serum myoglobin and acute kidney injury as well as the 30-day mortality were assessed.ResultsOverall, 268(67.3%) patients had acute kidney injury (KDIGO stage 1 or higher) and 75(18.8%) had severe acute kidney injury (KDIGO stage 2&3). Patients who developed acute kidney injury had higher level of perioperative serum myoglobin than patients without acute kidney injury. After adjusting for known acute kidney injury risk factors, logarithmically transformed preoperative serum myoglobin [OR = 1.58 (95% CI, 1.26-1.95), P < 0.001] and postoperative day 1 serum myoglobin [OR = 3.47 (95%CI, 2.27-5.29), P < 0.001] were associated with severe acute kidney injury. These correlation persisted after adjustment for decline in filtration via change in serum creatinine (ΔCr) and biomarkers of cardiac and kidney injury, including N-terminal prohormone of brain natriuretic peptide, cardiac troponin I, creatine kinase-MB, serum creatinine and Cystatin C. Compared with the clinical model, sMb considerably improved the risk discrimination and reclassification for AKI.ConclusionFor stanford type A aortic dissection patients underwent total aortic arch replacement with frozen elephant trunk implantation, serum myoglobin can improve postoperative acute kidney injury risk classification. Rhabdomyolysis may be an important supplement to the existing knowledge on the mechanism of acute kidney injury.

Project description:BackgroundGuidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches.MethodsA total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included. Ground truth (GT) WAs for 80 scans were segmented by a radiologist using an open-source software. The remaining 126 GT WAs were generated via semi-automatic segmentation process in which an ensemble of 3D convolutional neural networks (CNNs) aided the radiologist. Using 136 scans for training, 30 for validation, and 40 for testing, 2D and 3D CNNs were trained to automatically segment WA. Main evaluation metrics for outer surface extraction and segmentation accuracy were normalized surface Dice (NSD) and Dice coefficient score (DCS), respectively.Results2D CNN outperformed 3D CNN in NSD score (0.92 versus 0.90, p = 0.009), and both CNNs had equal DCS (0.96 versus 0.96, p = 0.110). Manual and semi-automatic segmentation times of one CTA scan were approximately 1 and 0.5 h, respectively.ConclusionsBoth CNNs segmented WA with high DCS, but based on NSD, better accuracy may be required before clinical application. CNN-based semi-automatic segmentation methods can expedite the generation of GTs.Relevance statementDeep learning can speeds up the creation of ground truth segmentations. CNNs can extract the outer aortic surface in patients with type B aortic dissection.Key points• 2D and 3D convolutional neural networks (CNNs) can extract the outer aortic surface accurately. • Equal Dice coefficient score (0.96) was reached with 2D and 3D CNNs. • Deep learning can expedite the creation of ground truth segmentations.

Project description:ObjectiveAcute aortic dissection is a life-threatening condition. Thoracic endovascular aortic repair (TEVAR), together with optimized medical treatment, is currently the first line treatment for acute Stanford type B aortic dissection. TEVAR can close the entry tear and reduce mortality. Aortic remodeling after TEVAR can directly affect the patient's long-term prognosis. The factors that influence aortic remodeling have, however, received insufficient clinical attention and remain unclear. It is very important to identify these factors.MethodsA total of 100 patients were continuously enrolled from 2011 to 2018 in 2 centers. Relevant data, including time from hospital admission to surgery, medicine use and aortic computed tomography angiography images obtained before and 6 months after surgery were collected. Patients were divided into favorable and adverse aortic remodeling groups, according to the degree of aortic remodeling. Analysis of variance and the chi-square test were performed using SPSS software to compare differences between groups and to determine the factors that influence postoperative aortic remodeling.ResultsThe proportion of single-stent implantations was higher in the favorable remodeling group than in the adverse remodeling group (79.5% vs. 53.8% in distal end of stent-graft level and 81.3% vs. 56.4% in diaphragm level, respectively, p < 0.05). The earlier the TEVAR procedure was performed, the better the aortic remodeling (3.4 days vs. 4.8 days in distal stent graft levels, and 3.6 days vs. 4.9 days in diaphragm level, respectively, p < 0.05), the presence of residual distal entry tears in the abdominal aorta also improved aortic remodeling after TEVAR (85.7% vs. 55.1% in the celiac trunk level, and 92.0% vs. 48.9% in the right renal artery level, respectively, p < 0.05).ConclusionSingle stent-graft implantation and early surgery were associated with favorable aortic remodeling. Distal entry tears were also conducive to aortic remodeling after surgery for aortic dissection.

Dataset Information

Identification of ferroptosis-associated biomarkers in Stanford type A aortic dissection based on machine learning.

Background

Methods

Results

Conclusion

Publications

Identification of ferroptosis-associated biomarkers in Stanford type A aortic dissection based on machine learning.

Similar Datasets

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